Inoculating an Infodemic: An Ecological Approach to Understanding Engagement With COVID-19 Online Information
Bibliographic record
Abstract
As the global COVID-19 pandemic has been concurrently labelled an “infodemic,” researchers have sought to improve how the general public engages with information that is relevant, timely, and accurate. In this study, we provide an overview of the reasons why people engage and disengage with COVID-19 information. We use context-rich semi-structured interviews which invited participants to discuss online COVID-19-related content they encountered. This qualitative approach allows us to uncover subtle but important details of influences that drive online engagement. Participants both engaged and disengaged with content for individual and social reasons, with seven themes emerging connected to their engagement including actions in response to information, reasoning for engagement, content, motivating concerns, frequency of engagement with information, site of exposure, and given reason for not engaging. Many of these themes intersected and informed each other. Our findings suggest that researchers and public health communicators should approach engagement as an ecology of intersecting influences, both human and algorithmic, which change over time. This information could be potentially helpful to public health communicators who are trying to engage the public with the best information to keep them safe during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".